Singular value decomposition in cointegration analysis: a note regarding the difference stationary series

Many methods and techniques have been developed gradually to compute cointegration vectors. We present here a comparatively simple method for computing the matrix of cointegrating vectors, by applying singular value decomposition. With this method, one can easily accommodate in the cointegrating vectors any deterministic factors, such as a dummy, apart from the constant term and the trend. Besides the errors corresponding to the finally selected cointegrating vector have the property of minimum variance. It is noted that this procedure is not mentioned in the relevant literature. Additionally, a sequential technique is introduced, for determining the order of integration for a given series. With this procedure one can directly detect whether the differencing process produces a stationary series or not, since it seems to be a common belief that differencing a variable (one or more times) we will always get a stationary series [Harris, R.: Using Cointegration Analysis in Econometric Modelling. Prentice Hall, London (1995)]. It will be seen that this is not necessarily the case.     

Rare shocks vs. non-linearities: What drives extreme events in the economy? Some empirical evidence

A small-scale vector autoregression (VAR) is used to shed some light on the roles of extreme shocks and non-linearities during stress events observed in the economy. The model focuses on the link between credit/financial markets and the real economy and is estimated on US quarterly data for the period 1984–2013. Extreme shocks are accounted for by assuming t-distributed reduced-form shocks. Non-linearity is allowed by the possibility of regime switch in the shock propagation mechanism. Strong evidence for fat tails in error distributions is found. Moreover, the results suggest that accounting for extreme shocks rather than explicit modeling of non-linearity contributes to the explanatory power of the model. Finally, it is shown that the accuracy of density forecasts improves if non-linearities and shock distributions with fat tails are considered.     

On the Conditional Variance of Fuzzy Random Variables

The conditional variance of random variables plays an important role for well-known variance decomposition formulas. In this paper, the conditional variance is defined for fuzzy random variables and some properties are proved, which especially generalize to the mentioned variance decomposition. Moreover, results for two special types of fuzzy random variables and an outlook for possible applications are presented.     

具有任意投资水平的供应链协同预测博弈研究

运用纳什均衡和贝叶斯更新模型,得到了供应链联合预测均衡的存在条件。模型中,供应商和零售商均需决定在预测技术上的投资水平,双方的需求预测将会被汇总成一个统一的预测。结果表明,双方预测能力越接近中等水平,越容易实现联合预测。预测能力偏离中等水平越远,越容易出现搭便车行为,即至少有一方不进行预测。     

The effect of spatiotemporal resolution on predictive policing model performance

Being able to anticipate crime such that new crime events can be dealt with effectively or prevented entirely, leads police forces worldwide to look at applying predictive policing, which provides predictions of times and places at risk for crime, such that proactive preventative measures can be taken. Ideally, predictive policing models predict crime at a high spatio-temporal level, while also providing optimal prediction performance. The main objective of this paper is therefore to evaluate the impact of varying grid resolution, temporal resolution and historical time frame on prediction performance. To investigate this, we analyse home burglary data from a large city in Belgium and predict new crime events using a range of parameter values, comparing the resulting prediction performances. Given the potential prediction performance costs associated with prediction at a high spatio-temporal resolution, consideration should be given to balance practical requirements with performance requirements.     

人口高密度地区强震伤亡GM模型研究

针对强烈地震事中伤亡人员救援问题,考虑此类突发事件受灾系统的贫信息特性,在现有灰预测研究成果基础上,构建一种面向人口高密度地区强震人员伤亡预测的灰色模型,设计了该模型参数的求解算法,以不定积分为工具,求得该模型的时间响应函数。数值计算结果表明新模型的有效性。     

Modeling and forecasting of Brazilian reservoir inflows via dynamic linear models

This work focuses on developing a forecasting model for the water inflow at an hydroelectric plant’s reservoir for operations planning. The planning horizon is 5 years in monthly steps. Due to the complex behavior of the monthly inflow time series we use a Bayesian dynamic linear model that incorporates seasonal and autoregressive components. We also use climate variables like monthly precipitation, El Niño and other indices as predictor variables when relevant. The Brazilian power system has 140 hydroelectric plants. Based on geographical considerations, these plants are collated by basin and classified into 15 groups that correspond to the major river basins, in order to reduce the dimension of the problem. The model is then tested for these 15 groups. Each group will have a different forecasting model that can best describe its unique seasonality and characteristics. The results show that the forecasting approach taken in this paper produces substantially better predictions than the current model adopted in Brazil (see Maceira & Damazio, 2006), leading to superior operations planning.     

Bayesian prediction with cointegrated vector autoregressions

A complete procedure for calculating the joint predictive distribution of future observations based on the cointegrated vector autoregression is presented. The large degree of uncertainty in the choice of cointegration vectors is incorporated into the analysis via the prior distribution. This prior has the effect of weighing the predictive distributions based on the models with different cointegration vectors into an overall predictive distribution. The ideas of Litterman [Mimeo, Massachusetts Institute of Technology, 1980] are adopted for the prior on the short run dynamics of the process resulting in a prior which only depends on a few hyperparameters. A straightforward numerical evaluation of the predictive distribution based on Gibbs sampling is proposed. The prediction procedure is applied to a seven-variable system with a focus on forecasting Swedish inflation.     

购房者认知价值对“小产权房”购买行为的影响——以武汉市为例

研究目的:揭示购房者认知价值对小产权房购买行为的影响,测度购房者认知价值各类因子对小产权房购买行为的影响方向和程度。研究方法:文献资料法、问卷调查法、因子分析法、结构方程模型法。研究结论:1.小产权房购房者认知价值因子可分为功能服务、购买成本、持有风险、心理诉求。2.功能服务、购买成本、心理诉求对小产权房购买行为动机层面有正向影响,其中功能服务的影响程度最大;购买成本、功能服务对小产权房购买行为决策层面有正向影响,持有风险对小产权房购买行为决策层面有负向影响,其中购买成本的影响程度最大。     

Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts

We construct a DSGE-VAR model for competing head to head with the long history of published forecasts of the Reserve Bank of New Zealand. We also construct a Bayesian VAR model with a Minnesota prior for forecast comparison. The DSGE-VAR model combines a structural DSGE model with a statistical VAR model based on the in-sample fit over the majority of New Zealand’s inflation-targeting period. We evaluate the real-time out-of-sample forecasting performance of the DSGE-VAR model, and show that the forecasts from the DSGE-VAR are competitive with the Reserve Bank of New Zealand’s published, judgmentally-adjusted forecasts. The Bayesian VAR model with a Minnesota prior also provides a competitive forecasting performance, and generally, with a few exceptions, out-performs both the DSGE-VAR and the Reserve Bank’s own forecasts.     

基于贝叶斯正则化神经网络的道路交通安全倾向性预测

对典型部门影响交通安全的相关因素进行了集成分析,构建了道路交通安全倾向性预测指标,在此基础上采用贝叶斯正则化神经网络对沈大高速公路某路段的道路交通安全倾向性进行了预测,对网络结构、训练集、预测集以及学习次数进行了优化.预测结果表明,在推广能力方面,贝叶斯正则化神经网络优于传统的神经网络,可作为探究道路交通安全系统内部各影响要素关系的辅助手段.